Channel Knowledge Map(CKM)-Assisted Multi-UAV Wireless Network:CKM Construction and UAV Placement

Channel knowledge map(CKM)has recently emerged as a viable new solution to facilitate the placement and trajectory optimization for unmanned aerial vehicle(UAV)communications,by exploiting the siteand location-specific radio propagation information.This paper investigates a CKM-assisted multi-UAV wireless network,by focusing on the construction and utilization of CKMs for multi-UAV placement optimization.First,we consider the CKM construction problem when data measurements for only a limited number of points are available.Towards this end,we exploit a data-driven interpolation technique,namely the Kriging method,to construct CKMs to characterize the signal propagation environments.Next,we study the multi-UAV placement optimization problem by utilizing the constructed CKMs,in which the multiple UAVs aim to optimize their placement locations to maximize the weighted sum rate with their respectively associated ground base stations(GBSs).However,the weighted sum rate function based on the CKMs is generally non-differentiable,which renders the conventional optimization techniques relying on function derivatives inapplicable.To tackle this issue,we propose a novel iterative algorithm based on derivative-free optimization,in which a series of quadratic functions are iteratively constructed to approximate the objective function under a set of interpolation conditions,and accordingly,the UAVs’placement locations are updated by maximizing the approximate function subject to a trust region constraint.Finally,numerical results are presented to validate the performance of the proposed designs.It is shown that the Kriging method can construct accurate CKMs for UAVs.Furthermore,the proposed derivative-free placement optimization design based on the Kriging-constructed CKMs achieves a weighted sum rate that is close to the optimal exhaustive search design based on ground-truth CKMs,but with much lower implementation complexity.In addition,the proposed design is shown to significantly outperform other benchmark schemes.

Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhauls

Unmanned aerial vehicles(UAVs)have emerged as a promising solution to provide wireless data access for ground users in various applications(e.g.,in emergency situations).This paper considers a UAVenabled wireless network,in which multiple UAVs are deployed as aerial base stations to serve users distributed on the ground.Different from prior works that ignore UAVs’backhaul connections,we practically consider that these UAVs are connected to the core network through a ground gateway node via rate-limited multi-hop wireless backhauls.We also consider that the air-to-ground access links from UAVs to users and the air-to-air backhaul links among UAVs are operated over orthogonal frequency bands.Under this setup,we aim to maximize the common(or minimum)throughput among all the ground users in the downlink of this network subject to the flow conservation constraints at the UAVs,by optimizing the UAVs’deployment locations,jointly with the bandwidth and power allocation of both the access and backhaul links.However,the common throughput maximization is a non-convex optimization problem that is difficult to be solved optimally.To tackle this issue,we use the techniques of alternating optimization and successive convex programming to obtain a locally optimal solution.Numerical results show that the proposed design significantly improves the common throughput among all ground users as compared to other benchmark schemes.